Our fastest model, EfficientFormer-L1, achieves \(79.2\%\)  top-1 accuracy on ImageNet-1K [1]} classification task with only \(1.6\)  ms inference time (averaged over \(1,000\)  runs), which runs as fast as MobileNetV2\(\times 1.4\)  and wields \(4.5\%\)  higher top-1 accuracy
(more results in Fig. REF  and Tab. REF ). The promising results demonstrate that latency is no longer an obstacle for the widespread adoption of vision transformers.
Our largest model, EfficientFormer-L7, achieves \(83.3\%\)  accuracy with only \(7.0\)  ms latency, outperforms ViT\(\times \) MobileNet hybrid designs (MobileViT-XS, \(74.8\%\) , \(7.2\) ms) by a large margin.
Additionally, we observe superior performance by employing EfficientFormer as the backbone in image detection and segmentation benchmarks (Tab. REF ).
We provide a preliminary answer to the aforementioned question, ViTs can achieve ultra fast inference speed and wield powerful performance at the same time. 
We hope our EfficientFormer can serve as a strong baseline and inspire followup works on the edge deployment of vision transformers.
